This formulation, known as Seq2Seq, originated from machine translation [1]} and assumes that the context-response pair in the training examples are fairly unique. Seq2Seq is the basis of most of the previous work on dialogue modeling. The framework, however, does not account for the semantic and syntactic redundancy in human conversations as pointed out by [2]} Li2016.
